The 5th International Workshop on Knowledge Discovery in Inductive Databases
(KDID’06) was held on September 18, 2006 in Berlin, Germany, in conjunction
with ECML/PKDD 2006: The 17th European Conference on Machine Learning
(ECML) and the 10th European Conference on Principles and Practice of
Knowledge Discovery in Databases (PKDD).
Inductive databases (IDBs) represent a database view on data mining and
knowledge discovery. IDBs contain not only data, but also generalizations (patterns
and models) valid in the data. In an IDB, ordinary queries can be used
to access and manipulate data, while inductive queries can be used to generate
(mine), manipulate, and apply patterns. In the IDB framework, patterns become
“first-class citizens” and KDD becomes an extended querying process in which
both the data and the patterns/models that hold in the data are queried.
The IDB framework is appealing as a general framework for data mining,
because it employs declarative queries instead of ad-hoc procedural constructs.
As declarative queries are often formulated using constraints, inductive querying
is closely related to constraint-based data mining. The IDB framework is also
appealing for data mining applications, as it supports the entire KDD process,
i.e., nontrivial multi-step KDD scenarios, rather than just individual data mining
operations. The goal of the workshop was to bring together database and
data mining researchers interested in the areas of inductive databases, inductive
queries, constraint-based data mining, and data mining query languages.
This workshop followed the previous four successful KDID workshops organized
in conjunction with ECML/PKDD: KDID’02 held in Helsinki, Finland,
KDID’03 held in Cavtat-Dubrovnik, Croatia, KDID’04 held in Pisa, Italy, and
KDID’05 held in Porto, Portugal. Its scientific program included nine regular
presentations and two short ones, as well as an invited talk by Kiri L. Wagstaff
(Jet Propulsion Laboratory, California Institute of Technology, USA). This volume
bundles all papers presented at the workshop and, in addition, includes
three contributions that cover relevant research presented at other venues. We
also include an article by one of the editors (SD) that attempts to unify existing
research in the area and outline directions for further research towards a general
framework for data mining.